Analysis of a Kind of Harr Characteristic Big Data Algorithm for College Students’ Safety Education Method

Abstract

In order to improve the effectiveness of safety education in colleges and universities and to maintain the long-term effectiveness of college students’ safety awareness, combined with the current safety problems in colleges and universities, by analyzing the relevance of campus safety accidents and safety education, a Harr characteristic big data algorithm for college students is proposed. Safety education methods have constructed a safety education system for college students. The status quo of safety education is analyzed for college students, a safety education system image acquisition module is designed based on Harr characteristic big data algorithm, Harr characteristic big data algorithm and Adaboost algorithm are used to shorten the training time of college students’ safety education image, learning resources are saved, and remote teaching is realized. A set of safety knowledge question banks based on recommendation algorithm has developed a set of practical safety knowledge online learning and testing systems, and targeted solutions to prevent and reduce campus safety accidents are proposed. The experimental results show that the number of student safety incidents in the school within 1 year after the application of this system is significantly reduced to 58, the system’s experimental response time is only 0.05 s on average, and the student’s satisfaction with the system reaches 93%. The application effect of the system is obvious, and it can effectively prevent and reduce the occurrence of campus safety accidents.